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Background <p>Specific learning difficulties (SpLD), ADHD, and ASD are the most common neurodevelopmental disorders (NDDs) found in mainstream schools. In addition to formal NDD diagnoses, children may exhibit NDD symptoms without meeting full diagnostic criteria, and heterogeneities and commonalities are frequently observed regardless of whether children have been diagnosed or not, raising concerns about the lack of inclusive support for all. Following the transdiagnostic approach and biopsychosocial model, this study aims to cluster and profile children with these symptoms via cognitive, psychological, and ecological factors, using an unsupervised machine learning algorithm.</p> Methods <p>Based on parent-report checklists, 267 Chinese primary school children in Grades 1–4 with at least one type of NDD symptoms were identified (164 boys; age in months: <i>M</i> = 102, <i>SD</i> = 17.30) from a bigger dataset (<i>N</i> = 1,034). A typically developing (TD) control group was created and matched with the NDD group in terms of age, gender, nonverbal IQ, and family socioeconomic status (SES). By using exploratory and confirmatory analyses, executive functioning, visual processing, and linguistic skills were extracted as cognitive factors, while internalising problems, externalising problems, positive child-parent relationships, and negative child-parent relationships were extracted as psychological and ecological factors.</p> Results <p>K-means clustering based on the seven extracted core factors identified five distinct clusters. Three clusters exhibited specific cognitive weaknesses, while the other two mainly showed psychosocial problems. Two severe-symptom groups (i.e., the Linguistic Difficulties group and the Psychosocial Difficulties group) also demonstrated worse academic and mental health outcomes.</p> Conclusions <p>Our findings demonstrate the potential to focus on symptoms beyond diagnostic labels, as well as the inclusion of psychosocial factors alongside cognitive ones, thereby contributing to the design of more targeted and comprehensive support for children with special education needs and informing current inclusive education practice in China.</p> Clinical trial number <p>Not applicable.</p>

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Profiling Chinese children with symptoms of SpLD, ADHD, or ASD: a transdiagnostic and biopsychosocial study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Duo Liu,
  • Ziqian Wei,
  • Urs Maurer,
  • Kevin Kien Hoa Chung,
  • Jesus Alfonso D. Datu,
  • Liory Fern-Pollak,
  • Taeko Wydell,
  • Winnie Wan-Yee Tso,
  • Fenghua Sun,
  • Lirong Luo,
  • Li-Chih Wang,
  • Yen Na Yum,
  • Guandong Xu,
  • Su Li

摘要

Background

Specific learning difficulties (SpLD), ADHD, and ASD are the most common neurodevelopmental disorders (NDDs) found in mainstream schools. In addition to formal NDD diagnoses, children may exhibit NDD symptoms without meeting full diagnostic criteria, and heterogeneities and commonalities are frequently observed regardless of whether children have been diagnosed or not, raising concerns about the lack of inclusive support for all. Following the transdiagnostic approach and biopsychosocial model, this study aims to cluster and profile children with these symptoms via cognitive, psychological, and ecological factors, using an unsupervised machine learning algorithm.

Methods

Based on parent-report checklists, 267 Chinese primary school children in Grades 1–4 with at least one type of NDD symptoms were identified (164 boys; age in months: M = 102, SD = 17.30) from a bigger dataset (N = 1,034). A typically developing (TD) control group was created and matched with the NDD group in terms of age, gender, nonverbal IQ, and family socioeconomic status (SES). By using exploratory and confirmatory analyses, executive functioning, visual processing, and linguistic skills were extracted as cognitive factors, while internalising problems, externalising problems, positive child-parent relationships, and negative child-parent relationships were extracted as psychological and ecological factors.

Results

K-means clustering based on the seven extracted core factors identified five distinct clusters. Three clusters exhibited specific cognitive weaknesses, while the other two mainly showed psychosocial problems. Two severe-symptom groups (i.e., the Linguistic Difficulties group and the Psychosocial Difficulties group) also demonstrated worse academic and mental health outcomes.

Conclusions

Our findings demonstrate the potential to focus on symptoms beyond diagnostic labels, as well as the inclusion of psychosocial factors alongside cognitive ones, thereby contributing to the design of more targeted and comprehensive support for children with special education needs and informing current inclusive education practice in China.

Clinical trial number

Not applicable.